273 citations · 386 across the 11 of their papers we have counts for
11 papers
Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data
Rui Xu, Yong Luo, Bo Du
Cross-domain pulmonary nodule detection suffers from performance degradation due to a large shift of data distributions between the source and target domain. Besides, considering t…
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning
Rui Xu, Lei Xing, Shuai Shao +4
Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted considerable attention in recent years. A popular FSL framework contains two phases: (i) the…
SSDL: Self-Supervised Dictionary Learning
Shuai Shao, Lei Xing, Wei Yu +3
The label-embedded dictionary learning (DL) algorithms generate influential dictionaries by introducing discriminative information. However, there exists a limitation: All the labe…
MDFM: Multi-Decision Fusing Model for Few-Shot Learning
Shuai Shao, Lei Xing, Rui Xu +3
In recent years, researchers pay growing attention to the few-shot learning (FSL) task to address the data-scarce problem. A standard FSL framework is composed of two components: i…
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
Shuai Shao, Lei Xing, Yan Wang +4
Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL framework is composed of two components: (1) Pre-train. Employ the base data to generate a CNN-based…
DLDL: Dynamic Label Dictionary Learning via Hypergraph Regularization
Shuai Shao, Mengke Wang, Rui Xu +2
For classification tasks, dictionary learning based methods have attracted lots of attention in recent years. One popular way to achieve this purpose is to introduce label informat…
SAHDL: Sparse Attention Hypergraph Regularized Dictionary Learning
Shuai Shao, Rui Xu, Yan-Jiang Wang +2
In recent years, the attention mechanism contributes significantly to hypergraph based neural networks. However, these methods update the attention weights with the network propaga…
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia +4
We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The tech…
Learning Symbolic Physics with Graph Networks
Miles D. Cranmer, Rui Xu, Peter Battaglia +1
We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our expe…
Label Embedded Dictionary Learning for Image Classification
Shuai Shao, Yan-Jiang Wang, Bao-Di Liu +2
Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification. The objective function of LC-KSVD is consisted of reconstruction error,…
Class specific or shared? A cascaded dictionary learning framework for image classification
Yan-Jiang Wang, Shuai Shao, Rui Xu +2
Dictionary learning methods can be split into: i) class specific dictionary learning ii) class shared dictionary learning. The difference between the two categories is how to use d…